Cut M&E Datacenter Costs with DataSphere
Posted in tech
In this blog series, we discuss how Media and Entertainment companies working in visual effects (VFX) and post production can use DataSphere to simplify scale-out architectures and make existing resources more powerful and effective. The series is comprised of three posts that compare a DataSphere scale-out system to a leading traditional scale out system.
This post describes how the DataSphere scale-out architecture reduces infrastructure needs, expands storage choice and increases operational efficiency to dramatically reduce M&E datacenter costs. The previous posts discussed how DataSphere improves performance and manageability. To help understand where all the savings come from as explained in this post, let’s review highlights from our previous blogs.
For background, DataSphere is a data virtualization platform that creates a global dataspace of storage resources spanning file, block and object protocols. It enables enterprises to orchestrate data non-disruptively across all of their storage resources according to IT-defined storage performance, protection and price requirements.
The performance blog post in this series described how the DataSphere scale-out architecture removes common performance chokepoints and eliminates the need for separate caching appliances. It also enables storage of all types to be deployed and uses that storage efficiently. This can dramatically reduce the amount of hardware and software customers require in their scale out environment, cutting capital and operating costs significantly.
DataSphere Expands Storage Options and Efficiency
The manageability blog post notes that traditional scale-out systems typically support just two storage tiers: performance and capacity. In contrast, DataSphere makes it possible to add storage of any type, from NVMe flash in servers to mid-range SAN or NAS storage to Amazon S3 buckets. This enables organizations to deploy the ideal storage to meet any data requirements.
In addition, DataSphere non-disruptively orchestrates data across all storage resources to place data on the right tier, according to IT-defined objectives and data’s actual access patterns. This ensures only active data consumes high performance storage, which greatly reduces the capacity organizations need to purchase and the need to overprovision, as capacity can easily be deployed as needed. Costs for lower cost capacity storage can also be reduced since DataSphere can keep data for completed projects accessible and visible in the cloud, eliminating a common hurdle in M&E environments—the need to keep data available for future projects.
DataSphere Automates Troubleshooting and Remediation
The manageability blog post describes how DataSphere makes data management easy by delivering comprehensive visibility into application’s data access and storage use, while automating remediation of hot spots non-disruptively across any storage customers choose to deploy. This greatly reduces IT management work, makes migrations and upgrades much easier, and eliminates many fire drills that ruin many IT nights and weekends. DataSphere’s automated remediation ensures that SLAs are met, time spent fixing problems decreases, and ticket count drops dramatically.
Save Up to 64% on Existing Infrastructure with DataSphere
A recent article in The Register noted that a global M&E leader expected to cut storage costs 64%, from $13,280,000 to $4,831,567 with DataSphere. These savings came from:
· Reduced over-provisioning of tier one storage by moving cold data to second tier
· Implementation of a global name space and the capability to move movie assets to a long-term storage tier, such as disk, tape or the cloud.
· Reducing the cost of adopting new storage
Figure 1. M&E Customers Cut Costs 64% with DataSphere.
DataSphere dramatically reduces costs with an efficient scale-out architecture that automates data placement on the right storage tier for data’s use. DataSphere reduces datacenter hardware, software, and management requirements, while increasing agility to reduce expensive storage overprovisioning.
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